Multi-Omics Integration: Definitions, Goals, and Open Questions

What is Multi-Omics?

Definition: Integration of multiple high-throughput molecular profiling technologies, each measuring a different layer of cellular regulation.

Core Omics Types:

• Genomics: DNA sequence and variation

• Epigenomics: DNA modifications and chromatin structure

• Transcriptomics: RNA expression levels

• Proteomics: Protein abundance and modifications

• Metabolomics: Small molecule metabolites

Extended Definition (2026): Also includes imaging data (radiomics), flow cytometry (CyTOF), spatial information, and clinical/phenotypic co-variates.

Two Primary Goals of Multi-Omics Studies

1. Sample-Focused Analysis: Classify and understand biological samples

2. Feature-Focused Analysis: Identify relationships between molecular features across layers

Goal 1: Sample-Focused Analysis

Objective: Improve classification and stratification of biological samples

Unsupervised Approaches:

• Integrative clustering to discover sample groupings

• Latent factor models to extract underlying variation patterns

• Example: Identifying cancer subtypes from multi-omics profiles

Supervised Approaches:

• Predict clinical outcomes or other response variables

• Identify biomarkers associated with outcomes

• Example: Precision medicine treatment selection

Key Advantage: Captures complex relationships across data types that single-omics approaches miss

Goal 2: Feature-Focused Analysis

Objective: Understand regulatory mechanisms across molecular layers

Approaches:

• Identify relationships between specific feature pairs (e.g., methylation-expression)

• Build multilayered regulatory networks

• Infer how regulation flows from DNA → RNA → Protein → Metabolite

Examples from Literature:

• Gene expression and methylation studies

• Transcriptome-wide association studies (TWAS): linking genetic variants to expression

• Metabolic flux balance analysis with transcriptomics integration

Ultimate Goal: Systems biology models that explain molecular mechanisms of health and disease

Analysis Strategies: Independent vs. Integrative

Historical Approach: Analyze each omics independently, then combine results

• Easy to implement

• Misses cross-layer interactions

• Less statistical power

Modern Integrative Approaches:

Meta-analysis methods: Combine statistical evidence across layers

Bayesian methods: Model relationships with prior knowledge

Latent factor analysis: Extract shared variation patterns (e.g., MOFA)

Machine/deep learning: Pattern recognition across modalities

Regression-based: Model one layer as function of others (e.g., mixOmics)

Hybrid Example: mixOmics performs outcome prediction AND builds co-regulation networks

Key Open Questions in Multi-Omics

Methodological Questions:

• How do we move from correlation to causation in multi-omics networks?

• How should temporal dynamics be integrated into multi-omics models?

• What is the optimal experimental design for different research questions?

Biological Questions:

• Which regulatory relationships are consistent across contexts vs. condition-specific?

• How do post-translational modifications fit into multi-omics regulatory models?

• Can we build predictive models of cellular state from multi-omics data?

Practical Questions:

• What is the minimum viable multi-omics experiment for a given question?

• How do we validate multi-omics biomarkers for clinical use?

• When does multi-omics provide value beyond well-executed single-omics?

Major Challenge: Heterogeneity Across Omics

Different technologies have vastly different properties:

• Signal-to-noise ratios vary widely

• Number of detected features differs by orders of magnitude

• Coverage of molecular space is incomplete and biased

• Statistical power varies substantially across platforms

Critical Implication: Lack of detected association may reflect technical limitations rather than biological absence

Major Challenges: Summary

Four Key Challenge Areas:

1. Missing Values: Incomplete sample coverage, platform limitations, technical failures

2. Interpretability: Difficulty building queryable systems models from complex multi-omics data

3. Data Sharing: Distributed storage, inconsistent annotation, lack of standards

4. Computational Performance: Scalability, resource requirements, need for cloud infrastructure

Single-Cell Multi-Omics: Future Opportunities

Why Single-Cell Multi-Omics Matters:

• Accounts for cell-type heterogeneity (crucial for tumors, brain, immune system)

• Enables cell-type-specific regulatory models

• Links molecular states to cellular phenotypes

Current Technologies:

• Parallel methods: measure multiple omics from the same cell (e.g., 10x Multiome, CITE-seq)

• Non-parallel methods: integrate datasets across modalities using computational matching

New Dimensions Available at Single-Cell Level:

• CRISPR perturbations

• Spatial localization

• Lineage tracing

• Trajectory inference

Single-Cell Multi-Omics: Challenges Amplified

Technical Challenges:

• Extreme sparsity, especially in scATAC-seq

• Limited protein capture (targeted panels only)

• Low read coverage per cell

• Technology-specific noise and bias

Analytical Challenges:

• Cell-type matching across non-parallel datasets

• Handling dropout and missing values

• Computational scaling (millions of cells)

• Building interpretable models from sparse single-cell data

Major Update Since 2021: Spatial omics revolution preserves tissue architecture and cell-cell interactions

Where is the Field Going?

Emerging Consensus Areas:

• Cloud computing is becoming standard for large-scale analysis

• Latent factor models are widely adopted for integration

• Single-cell multi-omics is replacing bulk approaches

• Spatial information is increasingly recognized as essential

Persistent Debates:

• Supervised vs. unsupervised approaches for biomarker discovery

• When to use complex integration vs. simpler separate analyses

• How to validate multi-omics findings across cohorts

• Path from research findings to clinical implementation

Technology Trends (Post-2021):

• Foundation models and large language models for biology

• Improved proteomics coverage (Olink, SomaScan)

• Multi-omics single-cell kits becoming commercially available

• Knowledge graphs for systems biology integration

Open Questions for Series Discussion

For Cancer Biology:

• Which cancer questions truly require multi-omics approaches?

• How do we integrate TCGA data (good genomics/transcriptomics) with limited proteomics/metabolomics?

• Can multi-omics predict therapy resistance better than genomics alone?

For Methods Development:

• How do we assess whether integration methods add value vs. single-omics?

• What validation strategies establish confidence in multi-omics biomarkers?

• How should we handle the tradeoff between model complexity and interpretability?

For Clinical Translation:

• What is the path from multi-omics biomarker discovery to FDA approval?

• How do we design cost-effective clinical multi-omics assays?

• Which omics provide the most value for specific clinical questions?

Planning the Multi-Omics Integration Series

Proposed Session Topics:

Session 1 (Today): Definitions, goals, and landscape overview

Session 2: Statistical methods deep-dive (latent factors, Bayesian approaches, regularization)

Session 3: Cancer applications (TCGA multi-omics, precision oncology case studies)

Session 4: Single-cell multi-omics technologies and analysis methods

Session 5: Spatial multi-omics and tumor microenvironment

Session 6: Clinical translation challenges and regulatory considerations

Session 7: Practical analysis demonstration OR AI/foundation models in multi-omics

For Each Session: Balance between methods understanding and biological interpretation

References & Resources

Original Paper:

Tarazona, S., Arzalluz-Luque, A. & Conesa, A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nat Comput Sci 1, 395-402 (2021).

Suggested Additional Reading for Series:

• Spatial omics reviews (2023-2024)

• Single-cell multi-omics methods reviews

• TCGA PanCancer Atlas papers

• Clinical multi-omics biomarker validation studies

Key Tools to Explore:

• mixOmics (R package for integration and visualization)

• MOFA/MOFA+ (latent factor models in Python/R)

• Seurat (single-cell integration in R)

• Scanpy (Python single-cell ecosystem)